While goods can be produced and stocked, services are not delivered until customers encounter service providers. To the extent that providers are often capacitated, delays are commonly experienced by customers in their access to services. Advances in information and mobile technologies have spawned novel mechanisms for managing waiting customers. These mechanisms emerge in public and private sectors, in established service operations as well as startup companies. My dissertation develops theoretical models to understand (1) how individual customers, as rational economic agents, respond to these mechanisms; (2) what are the consequences and implications for the aggregate system performance; (3) how to design these mechanisms most effectively; (4) how these new mechanisms compare with existing ones., ,Chapter 1 studies the optimal mechanism design of a two-sided marketplace where customers in a queue consensually trade their waiting spots. If a customer ever moves back in the queue, she will receive an appropriate monetary compensation. Customers can always decide not to participate in trading and retain their positions as if they are being served in the first-in, first-out (FIFO) queue discipline. The time-trading mechanism has the best of both worlds because it respects customers' property rights over their waiting positions as in FIFO and improves efficiency as in priority queues that serve customers with higher waiting costs. Chapter 1 designs the optimal mechanisms for the social planner, the service provider, and an intermediary who might mediate the trading platform. Both the social planner's and the service provider's optimal mechanisms involve a flat admission fee and an auction that implements strict priority. If a revenue-maximizing intermediary operates the trading platform, it should charge a trade participation fee and implement an auction with some trade restrictions. Therefore, customers are not strictly prioritized. However, relative to a FIFO system, the intermediary delivers value to the social planner by improving efficiency, and to the service provider by increasing its revenue., ,Chapter 2 examines a setting in which customers looking for service providers face search frictions and service providers vary in quality and availability. To understand customers' search behavior when they are confronted with a large collection of vertically differentiated, congested service providers, I build a model in which arriving customers conduct costly sequential search to resolve uncertainty about service providers' quality and queue length and select one according to an optimal stopping rule. Customers search due, in part, to variations in waiting time across service providers, which, in turn, are determined by the search behavior of customers. Thus, an equilibrium emerges. I characterize customers' equilibrium search/join behavior in a mean field model as the number of service providers grows large. I find that reducing either the search cost or customer arrival rate may not improve customer welfare and may strictly increase the average waiting time in the system as customers substitute toward high-quality service providers. Moreover, with lower search costs, the improved quality obtained by customers may not make up for the prolonged wait, therefore degrading the average search reward. Chapter 2 discusses policy implications of the results in the context of public surgical waits., ,Chapter 3 investigates the referral priority program, an emerging business practice adopted by a growing number of technology companies that manage a waitlist of customers, which enables existing customers on the waitlist to gain priority access if they successfully refer new customers to the waitlist. Unlike more commonly used referral reward programs, this novel mechanism does not offer monetary compensation to referring customers, but leverages customers' own disutility of delays to create referral incentives. Despite this appealing feature, the queueing-game-theoretic analysis in Chapter 3 finds the effectiveness of such a scheme as a marketing tool for customer acquisition and an operational approach for waitlist management depends crucially on the underlying market conditions, particularly the base market size of spontaneous customers. The referral priority program might not generate referrals when the base market size is either too large or too small. When customers do refer, the program could actually backfire, namely, by reducing the system throughput and customer welfare, if the base market size is intermediately large. This phenomenon occurs because the presence of referred customers severely cannibalizes the demand of spontaneous customers. I also compare the referral priority program with the referral reward program when the service provider optimally sets the admission price. I find that under a small base market size, the referral reward program would encourage referrals using monetary incentives. Numerical studies suggest the referral priority program is more profitable than the referral reward program when the base market size is intermediately small.